assessing impact of analysis approaches: all_retest
## libraries
library(colorout)
library(here)
library(data.table)
source(here("src", "stroop-rsa-pc.R"))
#render_report("optim_analysis", src_dir = "group_analyses/optim_2021-11-15", params = list(subjlist = "ispc_retest"))
#render_report("optim_analysis", src_dir = "group_analyses/optim_2021-11-15", params = list(subjlist = "all_retest"))
library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggridges)
library(abind)
library(httpgd)
## settings
hgd()## httpgd server running at:
## http://127.0.0.1:58905/live?token=6SflLGrJopts_chunk$set(echo = TRUE)
theme_set(theme_bw(base_size = 10))
## constants
#subjlist <- "ispc_retest"
subjlist <- params$subjlist
if (subjlist == "ispc_retest") {
session <- "reactive"
} else if (subjlist == "all_retest") {
session <- "proactive"
}
prewhs <- c("none", "obsall")
glms <- c("lsall_1rpm", "lssep_1rpm")
roiset <- "Schaefer2018Dev"
measures <- c("cveuc", "crcor")
subjects <- fread(here("out", paste0("subjlist_", subjlist, ".txt")))[[1]]
atlas <- read_atlas(roiset)
## read subject RDMs:
rdm <- enlist(combo_paste(measures, prewhs, sep = "__"))
for (prewh in prewhs) {
for (measure in measures) {
rdm[[paste0(measure, "__", prewh)]] <- read_rdms(
.measure = measure, .prewh = prewh,
.glmname = "lsall_1rpm", .roiset = roiset, .waves = waves,
.session = session, .subjects = subjects,
.ttypes1 = intersect(ttypes_by_run[[session]]$run1, ttypes[["bias"]]),
.ttypes2 = intersect(ttypes_by_run[[session]]$run2, ttypes[["bias"]]),
.rois = names(atlas$rois)
)
}
}
## read regression weights:
dat <- enlist(combo_paste(measures, glms, sep = "__"))
for (measure in measures) {
for (glmname in glms) {
for (prewh in prewhs) {
if (prewh == "obsall" && glmname == "lssep_1rpm") next
file_name <-
construct_filename_weights(
measure = measure, subjlist = subjlist, glmname = glmname, roiset = roiset, prewh = prewh
)
dat[[paste0(measure, "__", glmname, "__", prewh)]] <- fread(file_name)
}
}
}
dat <- rbindlist(dat, idcol = "id")
dat <- separate(dat, id, c("measure", "glmname", "prewh"), sep = "__")
## calculate
## for group stats:
dat_sum <- dat %>%
group_by(prewh, measure, glmname, term, roi, subject) %>% ## average over waves
summarize(b = mean(b), .groups = "drop_last") %>%
summarize(
t_stat = t.test(b)$statistic,
p = t.test(b, alternative = "greater")$p.value,
.groups = "keep"
)
## for test-retest correlations:
dat_r <- dat %>%
pivot_wider(names_from = "wave", values_from = "b") %>% ## spread over waves
group_by(prewh, measure, glmname, term, roi) %>% ## correlate over subjects
summarize(r = cor(wave1, wave2))## `summarise()` has grouped output by 'prewh', 'measure', 'glmname', 'term'. You can override using the `.groups` argument.all_retest
cross-run correlation
for (mod in models$crcor) {
rdm_models <-
read_model_rdm(
model = mod, measure_type = "similarity", session = session, ttype_subset = "bias"
)
p <- rdm_models %>% melt_mat %>% plot_melted_mat + labs(title = mod) + theme(axis.text.x = element_text(angle = 90, hjust = 0))
print(p)
}
RDMs
rdm_crcor_none <- rdm$crcor__none[, , c("Vis", "SomMot", "core32", "LH_Cont_PFCl_6", "LH_Cont_Par_6"), , ]
apply(atanh(rdm_crcor_none), c("dim1", "dim2", "roi", "wave"), mean) %>%
tanh %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "group mean matrices: non-prewhitened")
rdm_crcor_none[, , , 1, ] %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "single-subject matrices: non-prewhitened")
rdm_crcor_obsall <- rdm$crcor__obsall[, , c("Vis", "SomMot", "core32", "LH_Cont_PFCl_6", "LH_Cont_Par_6"), , ]
apply(atanh(rdm_crcor_obsall), c("dim1", "dim2", "roi", "wave"), mean) %>%
tanh %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "group mean matrices: prewhitened")
rdm_crcor_obsall[, , , 1, ] %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "single-subject matrices: prewhitened")
group stats
non-prewhitened
dat_sum %>%
filter(measure == "crcor", prewh == "none") %>%
ggplot(aes(roi, t_stat, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")
dat %>%
filter(measure == "crcor", prewh == "none") %>%
ggplot(aes(roi, b, color = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")
dat_sum %>%
filter(measure == "crcor", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "t_stat") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")
prewhitened: lsall_1rpm
dat_sum %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, t_stat, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")
dat %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, b, color = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")
dat_sum %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "t_stat") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")
test-retest correlations
non-prewhitened
dat_r %>%
filter(measure == "crcor", prewh == "none") %>%
ggplot(aes(roi, r, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")
dat_r %>%
filter(measure == "crcor", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "r") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")
prewhitened: lsall_1rpm
dat_r %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, r, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")
dat_r %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "r") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")
table
non-prewhitened
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "crcor", term %in% c("conjunction", "distractor", "incongruency", "target"), prewh == "none") %>%
pivot_wider(names_from = c("glmname"), values_from = c("t_stat", "p", "r")) %>%
filter(p_lsall_1rpm < 0.01 | p_lssep_1rpm < 0.01) %>%
arrange(-t_stat_lsall_1rpm) %>%
kable## Joining, by = c("prewh", "measure", "glmname", "term", "roi")| prewh | measure | term | roi | t_stat_lsall_1rpm | t_stat_lssep_1rpm | p_lsall_1rpm | p_lssep_1rpm | r_lsall_1rpm | r_lssep_1rpm |
|---|---|---|---|---|---|---|---|---|---|
| none | crcor | target | SomMot | 6.036965 | 5.908287 | 0.0000007 | 0.0000010 | -0.0692325 | -0.1095208 |
| none | crcor | distractor | Vis | 5.854255 | 4.040639 | 0.0000012 | 0.0001791 | 0.0955291 | 0.1808343 |
| none | crcor | incongruency | core32 | 5.056794 | 3.232358 | 0.0000108 | 0.0015274 | 0.2739029 | 0.1343527 |
| none | crcor | incongruency | LH_Cont_Par_6 | 5.048706 | 4.768844 | 0.0000111 | 0.0000241 | 0.3087965 | 0.0790452 |
| none | crcor | incongruency | LH_Cont_Par_4 | 4.180174 | 4.081220 | 0.0001224 | 0.0001604 | 0.1563675 | 0.1924391 |
| none | crcor | incongruency | Vis | 3.898284 | 2.683145 | 0.0002635 | 0.0059601 | 0.4612235 | 0.1363985 |
| none | crcor | incongruency | SomMot | 3.561764 | 3.122884 | 0.0006477 | 0.0020188 | 0.1089093 | 0.1595941 |
| none | crcor | incongruency | LH_Cont_PFCl_6 | 3.538587 | 1.431759 | 0.0006886 | 0.0814516 | -0.0175133 | 0.0677992 |
| none | crcor | incongruency | RH_Cont_PFCl_12 | 3.422061 | 2.169139 | 0.0009349 | 0.0192055 | -0.1772072 | -0.0840016 |
| none | crcor | incongruency | RH_Cont_Par_6 | 3.384942 | 1.658137 | 0.0010299 | 0.0540347 | 0.0894960 | -0.1248471 |
| none | crcor | incongruency | RH_Cont_PFCl_9 | 3.331295 | 2.471558 | 0.0011837 | 0.0097834 | 0.1403675 | 0.1232427 |
| none | crcor | incongruency | RH_Cont_PFCl_14 | 3.300100 | 1.345269 | 0.0012831 | 0.0944821 | 0.0876670 | 0.0518206 |
| none | crcor | target | RH_Cont_PFCl_14 | 3.245631 | 1.956604 | 0.0014762 | 0.0300424 | -0.1872049 | 0.4136216 |
| none | crcor | distractor | SomMot | 3.216687 | 2.078356 | 0.0015899 | 0.0233162 | -0.1519217 | -0.0944269 |
| none | crcor | incongruency | RH_Cont_PFCmp_2 | 3.155265 | 1.965575 | 0.0018596 | 0.0294944 | -0.1478682 | 0.1127163 |
| none | crcor | incongruency | LH_Cont_Par_1 | 2.719606 | 1.952963 | 0.0054625 | 0.0302673 | 0.1814894 | 0.1319579 |
| none | crcor | incongruency | LH_Cont_PFCmp_1 | 2.640062 | 2.707613 | 0.0066026 | 0.0056217 | 0.3228169 | 0.3442514 |
| none | crcor | incongruency | RH_Cont_PFCv_1 | 2.602599 | 2.442684 | 0.0072130 | 0.0104528 | 0.0355817 | 0.1240820 |
| none | crcor | target | core32 | 2.506627 | 1.549322 | 0.0090234 | 0.0660749 | 0.0363741 | -0.1075845 |
| none | crcor | target | RH_SalVentAttn_TempOccPar_7 | 1.706360 | 2.541060 | 0.0493149 | 0.0083304 | -0.1185983 | -0.4011169 |
| none | crcor | incongruency | RH_Cont_Par_4 | 1.547407 | 2.693777 | 0.0663049 | 0.0058108 | -0.0021916 | 0.0951452 |
| none | crcor | target | LH_Cont_Par_5 | 1.408167 | 3.182853 | 0.0848562 | 0.0017334 | -0.0939326 | -0.1547981 |
prewhitened
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "crcor", term %in% c("conjunction", "distractor", "incongruency", "target"), prewh == "obsall") %>%
filter(p < 0.01) %>%
arrange(-t_stat) %>%
kable## Joining, by = c("prewh", "measure", "glmname", "term", "roi")| prewh | measure | glmname | term | roi | t_stat | p | r |
|---|---|---|---|---|---|---|---|
| obsall | crcor | lsall_1rpm | target | SomMot | 13.435313 | 0.0000000 | -0.1307399 |
| obsall | crcor | lsall_1rpm | distractor | Vis | 11.086140 | 0.0000000 | 0.5171092 |
| obsall | crcor | lsall_1rpm | target | Vis | 9.514942 | 0.0000000 | -0.2006751 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Cont_Par_4 | 4.238609 | 0.0001043 | 0.3495953 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Cont_Par_6 | 4.104229 | 0.0001506 | -0.0819877 |
| obsall | crcor | lsall_1rpm | incongruency | core32 | 3.942262 | 0.0002339 | 0.2108016 |
| obsall | crcor | lsall_1rpm | incongruency | Vis | 3.638746 | 0.0005282 | 0.1502920 |
| obsall | crcor | lsall_1rpm | incongruency | SomMot | 3.511743 | 0.0007390 | 0.2039571 |
| obsall | crcor | lsall_1rpm | target | core32 | 3.381300 | 0.0010397 | -0.1315888 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Cont_PFCl_7 | 3.345319 | 0.0011415 | -0.0778200 |
| obsall | crcor | lsall_1rpm | target | LH_Cont_Par_4 | 3.285087 | 0.0013338 | -0.3033117 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Default_Par_7 | 3.103258 | 0.0021216 | -0.1740947 |
| obsall | crcor | lsall_1rpm | incongruency | RH_Cont_PFCv_1 | 3.037992 | 0.0025003 | -0.0059184 |
| obsall | crcor | lsall_1rpm | incongruency | RH_Cont_PFCl_9 | 3.003695 | 0.0027244 | 0.2892933 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Default_PFC_17 | 2.979630 | 0.0028928 | -0.0040970 |
| obsall | crcor | lsall_1rpm | incongruency | RH_Cont_Par_3 | 2.830532 | 0.0041771 | -0.3734273 |
| obsall | crcor | lsall_1rpm | incongruency | RH_Cont_PFCmp_2 | 2.729695 | 0.0053318 | 0.0499868 |
| obsall | crcor | lsall_1rpm | target | LH_Cont_Par_6 | 2.648696 | 0.0064689 | 0.1505068 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Default_PFC_21 | 2.623932 | 0.0068593 | 0.2462755 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Cont_PFCl_3 | 2.558962 | 0.0079899 | 0.3134959 |
| obsall | crcor | lsall_1rpm | incongruency | LH_Cont_PFCmp_1 | 2.530552 | 0.0085365 | 0.1235462 |
All significant ROIs at \(\alpha < 0.01\) uncorrected. Sorted by t_stat_lsall_1rpm. r_* columns show test–retest correlations.
cross-validated euclidean
for (mod in models$cveuc) {
rdm_models <-
read_model_rdm(
model = mod, measure_type = "cvdistance", session = session, ttype_subset = "bias"
)
p <- rdm_models %>% melt_mat %>% plot_melted_mat + labs(title = mod) + theme(axis.text.x = element_text(angle = 90, hjust = 0))
print(p)
}
RDMs
rdm_cveuc_none <- rdm$cveuc__none[, , c("Vis", "SomMot", "core32", "LH_Cont_PFCl_6", "LH_Cont_Par_6"), , ]
sds <- apply(rdm_cveuc_none, c("roi", "subject", "wave"), function(x) sd(vec(x)))
rdm_cveuc_none <- sweep(rdm_cveuc_none, 3:5, sds, "/")
apply(rdm_cveuc_none, c("dim1", "dim2", "roi", "wave"), mean) %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "group mean matrices: non-prewhitened")
rdm_cveuc_none[, , , 1, ] %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "single-subject matrices: non-prewhitened")
rdm_cveuc_obsall <- rdm$cveuc__obsall[, , c("Vis", "SomMot", "core32", "LH_Cont_PFCl_6", "LH_Cont_Par_6"), , ]
sds <- apply(rdm_cveuc_obsall, c("roi", "subject", "wave"), function(x) sd(vec(x)))
rdm_cveuc_obsall <- sweep(rdm_cveuc_obsall, 3:5, sds, "/")
apply(rdm_cveuc_obsall, c("dim1", "dim2", "roi", "wave"), mean) %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "group mean matrices: prewhitened")
rdm_cveuc_obsall[, , , 1, ] %>%
melt_mat %>%
plot_melted_mat(.row = "dim1", .col = "dim2", add_greyscale_gradient = FALSE) +
scale_fill_viridis_c() +
facet_grid(vars(wave), vars(roi), scales = "free") +
theme(axis.text.x = element_text(angle = 90, hjust = 0), legend.position = "top") +
labs(title = "single-subject matrices: prewhitened")
group stats
non-prewhitened
dat_sum %>%
filter(measure == "cveuc", prewh == "none") %>%
ggplot(aes(roi, t_stat, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")
dat %>%
filter(measure == "cveuc", prewh == "none") %>%
ggplot(aes(roi, b, color = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")
dat_sum %>%
filter(measure == "cveuc", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "t_stat") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")
prewhitened: lsall_1rpm
dat_sum %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, t_stat, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")
dat %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, b, color = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")
dat_sum %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "t_stat") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")
test-retest correlations
non-prewhitened
dat_r %>%
filter(measure == "cveuc", prewh == "none") %>%
ggplot(aes(roi, r, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")
dat_r %>%
filter(measure == "cveuc", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "r") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")
prewhitened: lsall_1rpm
dat_r %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, r, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")
dat_r %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "r") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")
table
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "cveuc", prewh == "none", term %in% c("target", "distractor", "incongruency")) %>%
pivot_wider(names_from = c("glmname"), values_from = c("t_stat", "p", "r")) %>%
filter(p_lsall_1rpm < 0.01 | p_lssep_1rpm < 0.01) %>%
arrange(-t_stat_lsall_1rpm) %>%
kable## Joining, by = c("prewh", "measure", "glmname", "term", "roi")| prewh | measure | term | roi | t_stat_lsall_1rpm | t_stat_lssep_1rpm | p_lsall_1rpm | p_lssep_1rpm | r_lsall_1rpm | r_lssep_1rpm |
|---|---|---|---|---|---|---|---|---|---|
| none | cveuc | distractor | Vis | 5.096293 | 3.356673 | 0.0000097 | 0.0011084 | -0.0716702 | 0.2357591 |
| none | cveuc | incongruency | LH_Cont_PFCl_6 | 3.477796 | 1.083349 | 0.0008080 | 0.1437895 | -0.4927242 | -0.0715695 |
| none | cveuc | target | RH_Cont_Par_3 | 2.836896 | 1.361400 | 0.0041127 | 0.0919360 | 0.0193541 | 0.3095593 |
| none | cveuc | target | RH_Cont_PFCl_14 | 2.557936 | 2.632462 | 0.0080090 | 0.0067224 | 0.1051463 | 0.1044802 |
| none | cveuc | distractor | RH_Cont_Par_5 | 1.846091 | 2.470704 | 0.0375548 | 0.0098026 | -0.2331548 | 0.0484790 |
| none | cveuc | target | RH_SalVentAttn_TempOccPar_7 | 1.748702 | 2.664738 | 0.0454610 | 0.0062271 | 0.1283927 | -0.0385871 |
| none | cveuc | target | RH_Default_Par_4 | 1.421245 | 2.638739 | 0.0829552 | 0.0066233 | 0.0760200 | -0.0582209 |
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "cveuc", prewh == "obsall", term %in% c("target", "distractor", "incongruency")) %>%
filter(p < 0.01) %>%
arrange(-t_stat) %>%
kable## Joining, by = c("prewh", "measure", "glmname", "term", "roi")| prewh | measure | glmname | term | roi | t_stat | p | r |
|---|---|---|---|---|---|---|---|
| obsall | cveuc | lsall_1rpm | distractor | Vis | 12.466757 | 0.0000000 | 0.5122660 |
| obsall | cveuc | lsall_1rpm | target | SomMot | 11.741408 | 0.0000000 | 0.0058902 |
| obsall | cveuc | lsall_1rpm | target | Vis | 9.218406 | 0.0000000 | -0.2030414 |
| obsall | cveuc | lsall_1rpm | incongruency | SomMot | 3.961718 | 0.0002219 | -0.1000386 |
| obsall | cveuc | lsall_1rpm | target | core32 | 3.080917 | 0.0022446 | 0.0060724 |
| obsall | cveuc | lsall_1rpm | incongruency | LH_Cont_PFCl_6 | 3.014850 | 0.0026495 | 0.2587848 |
| obsall | cveuc | lsall_1rpm | incongruency | core32 | 2.904066 | 0.0034881 | -0.1025529 |
| obsall | cveuc | lsall_1rpm | target | LH_Cont_PFCl_6 | 2.625262 | 0.0068378 | 0.1245884 |
| obsall | cveuc | lsall_1rpm | incongruency | Vis | 2.573099 | 0.0077301 | 0.1591310 |
All significant ROIs at \(\alpha < 0.01\) uncorrected. Sorted by t_stat_lsall_1rpm. r_* columns show test–retest correlations.
comparison of measures: cross-validated euclidean and cross-run correlation, lsall_1rpm
non-prewhitened euclidean
dat_sum %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "none", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "t_stat") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "t statistics", x = "cross-run correlation", y = "cross-validated euclidean")
dat_r %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "none", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "r") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "test--retest correlations", x = "cross-run correlation", y = "cross-validated euclidean")
prewhitened euclidean
dat_sum %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "obsall", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "t_stat") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "t statistics", x = "cross-run correlation", y = "cross-validated euclidean")
dat_r %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "obsall", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "r") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "test--retest correlations", x = "cross-run correlation", y = "cross-validated euclidean")